Predictive value of pulmonary function parameters for sleep apnea syndrome

Pulmonary Function Test and Obstructive Sleep Apnea Hypopnea Syndrome in Obese Adults: A Retrospective Study

Objective

We explore risk factors related to severe obstructive sleep apnea (OSA) in obese patients, including pulmonary ventilation function, diffusion function, and impulse oscillometry (IOS) data.

Methods

The medical records of 207 obese patients who were prepared to undergo bariatric surgery in a hospital from May 2020 to September 2021 were retrospectively reviewed. Polysomnography (PSG), pulmonary ventilation function, diffusion function, and IOS parameters were collected according to the ethical standards of the institutional research committee (registration number: KYLL-202008-144). Logistic regression analysis was used to analyze the related independent risk factors.

Results

There were significantly statistical difference in a number of pulmonary ventilation and diffusion function parameters among the non-OSAHS group, the mild-to-moderate OSA group, and the severe OSA group. However, only airway resistance parameters R5%, R10%, R15%, R20%, R25%, and R35% increased with increasing OSA severity and were positively correlated with apnea hypopnea index (AHI). Age (P = 0.012, 1.104 (1.022, 1.192)), body mass index (P< 0.0001, 1.12 (1.057, 1.187)), gender (P = 0.003, 4.129 (1.625, 10.49)), and R25% (P = 0.007, 1.018 (1.005, 1.031)) were independent risk factors for severe OSA. In patients aged 35 to 60, RV/TLC (P = 0.029, 1.272 (1.025, 1.577)) is an independent risk factor for severe OSA.

Conclusion

R25% was an independent risk factor for severe OSA in obese individuals, while RV/TLC was also an independent risk factor in those aged 35 to 60. Pulmonary function tests (PFTs), particularly IOS levels, are recommended to assess severe OSA in obese patients.

Helping early obstructive sleep apnea diagnosis with machine learning: A systematic review (Preprint)

Background

American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.

Objective

We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA.

Methods

We searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study.

Results

Our search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression.

Conclusions

Although high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition.

Trial Registration

PROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339

Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample

STUDY OBJECTIVES

Polysomnography is the gold standard for diagnosis of obstructive sleep apnea (OSA) but it is costly and access is often limited. The aim of this study is to develop a clinically useful support vector machine (SVM)-based prediction model to identify patients with high probability of OSA for nonsleep specialist physician in clinical practice.

METHODS

The SVM model was developed using the features routinely collected at the clinical evaluation from 6,875 Chinese patients referred to sleep clinics for suspected OSA. Three apnea-hypopnea index (AHI) cutoffs, ≥5/h, ≥15/h, and ≥30/h were used to define the severity of OSA. The continuous and categorized features were selected separately and were further selected through stepwise forward feature selection. The modeling was achieved through fivefold cross-validation. The model discriminative ability was evaluated for the whole data set and four subgroups categorized with gender and age (<65 versus ≥65 years old [y/o]).

RESULTS

Two features were selected to predict AHI cutoff ≥5/h with six features selected for ≥15/h, and six features selected for ≥30/h, respectively, to reach Area under the Receiver Operating Characteristic (AUROC) 0.82, 0.80, and 0.78, respectively. The sensitivity was 74.14%, 75.18%, and 70.26%, while the specificity was 74.71%, 68.73%, and 70.30%, respectively. Compared to logistic regression, Berlin questionnaire, NoSAS Score, and Supersparse Linear Integer Model (SLIM) scoring system, the SVM model performs better with a more balanced sensitivity and specificity. The discriminative ability was best for male <65 y/o and modest for female ≥65 y/o.

CONCLUSION

Our model provides a simple and accurate modality for early identification of patients with OSA and may potentially help prioritize them for sleep study.

The Ventilatory and Diffusion Dysfunctions in Obese Patients with and without Obstructive Sleep Apnea-Hypopnea Syndrome

OBJECTIVE

To analyze the ventilatory and alveolar-capillary diffusion dysfunctions in case of obesity with or without an OSAS.

METHODS

It is a cross-sectional study of 48 obese adults (23 OSAS and 25 controls). Anthropometric data (height, weight, and body mass index (BMI)) were collected. All adults responded to a medical questionnaire and underwent polysomnography or sleep polygraphy for apnea-hypopnea index (AHI) and percentage of desaturation measurements. The following lung function data were collected: pulmonary flows and volumes, lung transfer factor for carbon monoxide (DLCO), and fraction of exhaled nitric oxide (F_eNO).

RESULTS

Obesity was confirmed for the two groups with a total sample mean value of BMI = 35.06 ± 4.68 kg/m². A significant decrease in lung function was noted in patients with OSAS compared with controls. Indeed, when compared with the control group, the OSAS one had a severe restrictive ventilatory defect (total lung capacity: 93 ± 14 vs. 79 ± 12%), an abnormal DLCO (112 ± 20 vs. 93 ± 22%), and higher bronchial inflammation (18.40 ± 9.20 vs. 31.30 ± 13.60 ppb) (p < 0.05).

CONCLUSION

Obesity when associated with OSAS increases the severity of pulmonary function and alveolar-capillary diffusion alteration. This can be explained in part by the alveolar inflammation.

Simulated intention-to-treat analysis based on clinical parameters of patients at high risk for sleep apnea derivated to respiratory polygraphy

Purpose

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAS) is a public health problem. We designed a pilot study to validate empiric indication of CPAP therapy in a population with moderate-to-high pre-test probabilities who underwent self-administered home-based respiratory polygraphy (RP).

Methods

A cross-sectional simulation study was performed. CPAP therapy could be indicated by two independent blind observers. Observer 1´s decision was based on the results of STOP-BANG (SBQ) and Epworth Sleepiness Scale (ESS) and Observer 2 used all objective data provided by RP + SBQ + ESS.

Results

We evaluated 1763 patients; 1060 men and 703 women (39.2%) with a mean age of 53.6±13.8 and a body mass index (BMI) of 32.8±7.5 kg/m2. We found evidence of mild (34.1%), moderate (26.6%), and severe (18.3%) There were Apnea-Hypopnea Index (AHI) relationship between > 5 or < 5 SBQ and RP AHI (p<0.05). BMI > 25 kg/m² + snoring (S) + observed apnea (O) + 1 of the following: ESS > 11, hypertension (HT) or > 5 SBQ components showed sensitivity of 40% (CI95%: 37.3-43) and specificity of 95.1% (CI93.4-96.4). The performance of 5 SBQ components with regard to gender and empirical CPAP therapy was; (women vs. men): AUC-ROC 0.625 (CI95%: 0.599-0.651) vs. 0.70 (CI95%: 0.68-0.72), p<0.01, respectively.

Conclusions

STOP-BANG and ESS made it possible to indicate CPAP reliably (low rate of false-positive results) in 20-40% of patients who needed such therapy according to clinical history and RP results. These clinical criteria performed better in male.

[Alterations of respiratory resistance in patients with obstructive sleep apnea hypopnea syndrome]

OBJECTIVE

To evaluate the association between the components of airway resistance and severity of obstructive sleep apnea hypopnea syndrome (OSAHS).

METHODS

A total of 234 patients with snoring during sleep underwent full-night polysomnography in our center between January, 2015 and September, 2017. According to the apnea-hypopnea index (AHI) scores, the patients were divided into non-OSAHS group (AHI scores <5), mild or moderate OSAHS group (5-30) group, and severe OSAHS group (>30). The pulmonary function and respiratory resistance of the patients were assessed using spirometry and impulse oscillometry, respectively, and the correlation between the parameters of respiratory resistance and the severity of AHI were analyzed.

RESULTS

The non-OSAHS, mild or moderate OSAHS, and severe OSAHS groups consisted of 31, 90 and 113 patients, respectively. The patients with severe OSAHS had significantly higher levels of respiratory resistance at 5 Hz (R5) and 20 Hz (R20), FEF_50% and MMEF than those in the other two groups (P<0.05). Bivariate correlation analysis identified positive correlations of R5 (r=0.259, P=0.000), R20 (r=0.298, P=0.000) and FEF_50% (r=0.176, P=0.007) with AHI scores of the patients.

CONCLUSION

Patients with OSAHS have increased respiratory resistance in the large airways and compensatory reduction in small airway resistance.

Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline

INTRODUCTION

This guideline establishes clinical practice recommendations for the diagnosis of obstructive sleep apnea (OSA) in adults and is intended for use in conjunction with other American Academy of Sleep Medicine (AASM) guidelines on the evaluation and treatment of sleep-disordered breathing in adults.

METHODS

The AASM commissioned a task force of experts in sleep medicine. A systematic review was conducted to identify studies, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) process was used to assess the evidence. The task force developed recommendations and assigned strengths based on the quality of evidence, the balance of benefits and harms, patient values and preferences, and resource use. In addition, the task force adopted foundational recommendations from prior guidelines as "good practice statements", that establish the basis for appropriate and effective diagnosis of OSA. The AASM Board of Directors approved the final recommendations.

RECOMMENDATIONS

The following recommendations are intended as a guide for clinicians diagnosing OSA in adults. Under GRADE, a STRONG recommendation is one that clinicians should follow under most circumstances. A WEAK recommendation reflects a lower degree of certainty regarding the outcome and appropriateness of the patient-care strategy for all patients. The ultimate judgment regarding propriety of any specific care must be made by the clinician in light of the individual circumstances presented by the patient, available diagnostic tools, accessible treatment options, and resources. Good Practice Statements: Diagnostic testing for OSA should be performed in conjunction with a comprehensive sleep evaluation and adequate follow-up. Polysomnography is the standard diagnostic test for the diagnosis of OSA in adult patients in whom there is a concern for OSA based on a comprehensive sleep evaluation.Recommendations: We recommend that clinical tools, questionnaires and prediction algorithms not be used to diagnose OSA in adults, in the absence of polysomnography or home sleep apnea testing. (STRONG). We recommend that polysomnography, or home sleep apnea testing with a technically adequate device, be used for the diagnosis of OSA in uncomplicated adult patients presenting with signs and symptoms that indicate an increased risk of moderate to severe OSA. (STRONG). We recommend that if a single home sleep apnea test is negative, inconclusive, or technically inadequate, polysomnography be performed for the diagnosis of OSA. (STRONG). We recommend that polysomnography, rather than home sleep apnea testing, be used for the diagnosis of OSA in patients with significant cardiorespiratory disease, potential respiratory muscle weakness due to neuromuscular condition, awake hypoventilation or suspicion of sleep related hypoventilation, chronic opioid medication use, history of stroke or severe insomnia. (STRONG). We suggest that, if clinically appropriate, a split-night diagnostic protocol, rather than a full-night diagnostic protocol for polysomnography be used for the diagnosis of OSA. (WEAK). We suggest that when the initial polysomnogram is negative and clinical suspicion for OSA remains, a second polysomnogram be considered for the diagnosis of OSA. (WEAK).

Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline

INTRODUCTION

This guideline establishes clinical practice recommendations for the diagnosis of obstructive sleep apnea (OSA) in adults and is intended for use in conjunction with other American Academy of Sleep Medicine (AASM) guidelines on the evaluation and treatment of sleep-disordered breathing in adults.

METHODS

The AASM commissioned a task force of experts in sleep medicine. A systematic review was conducted to identify studies, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) process was used to assess the evidence. The task force developed recommendations and assigned strengths based on the quality of evidence, the balance of benefits and harms, patient values and preferences, and resource use. In addition, the task force adopted foundational recommendations from prior guidelines as "good practice statements", that establish the basis for appropriate and effective diagnosis of OSA. The AASM Board of Directors approved the final recommendations.

RECOMMENDATIONS

The following recommendations are intended as a guide for clinicians diagnosing OSA in adults. Under GRADE, a STRONG recommendation is one that clinicians should follow under most circumstances. A WEAK recommendation reflects a lower degree of certainty regarding the outcome and appropriateness of the patient-care strategy for all patients. The ultimate judgment regarding propriety of any specific care must be made by the clinician in light of the individual circumstances presented by the patient, available diagnostic tools, accessible treatment options, and resources. Good Practice Statements: Diagnostic testing for OSA should be performed in conjunction with a comprehensive sleep evaluation and adequate follow-up. Polysomnography is the standard diagnostic test for the diagnosis of OSA in adult patients in whom there is a concern for OSA based on a comprehensive sleep evaluation.Recommendations: We recommend that clinical tools, questionnaires and prediction algorithms not be used to diagnose OSA in adults, in the absence of polysomnography or home sleep apnea testing. (STRONG). We recommend that polysomnography, or home sleep apnea testing with a technically adequate device, be used for the diagnosis of OSA in uncomplicated adult patients presenting with signs and symptoms that indicate an increased risk of moderate to severe OSA. (STRONG). We recommend that if a single home sleep apnea test is negative, inconclusive, or technically inadequate, polysomnography be performed for the diagnosis of OSA. (STRONG). We recommend that polysomnography, rather than home sleep apnea testing, be used for the diagnosis of OSA in patients with significant cardiorespiratory disease, potential respiratory muscle weakness due to neuromuscular condition, awake hypoventilation or suspicion of sleep related hypoventilation, chronic opioid medication use, history of stroke or severe insomnia. (STRONG). We suggest that, if clinically appropriate, a split-night diagnostic protocol, rather than a full-night diagnostic protocol for polysomnography be used for the diagnosis of OSA. (WEAK). We suggest that when the initial polysomnogram is negative and clinical suspicion for OSA remains, a second polysomnogram be considered for the diagnosis of OSA. (WEAK).

Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline

INTRODUCTION

This guideline establishes clinical practice recommendations for the diagnosis of obstructive sleep apnea (OSA) in adults and is intended for use in conjunction with other American Academy of Sleep Medicine (AASM) guidelines on the evaluation and treatment of sleep-disordered breathing in adults.

METHODS

The AASM commissioned a task force of experts in sleep medicine. A systematic review was conducted to identify studies, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) process was used to assess the evidence. The task force developed recommendations and assigned strengths based on the quality of evidence, the balance of benefits and harms, patient values and preferences, and resource use. In addition, the task force adopted foundational recommendations from prior guidelines as "good practice statements", that establish the basis for appropriate and effective diagnosis of OSA. The AASM Board of Directors approved the final recommendations.

RECOMMENDATIONS

The following recommendations are intended as a guide for clinicians diagnosing OSA in adults. Under GRADE, a STRONG recommendation is one that clinicians should follow under most circumstances. A WEAK recommendation reflects a lower degree of certainty regarding the outcome and appropriateness of the patient-care strategy for all patients. The ultimate judgment regarding propriety of any specific care must be made by the clinician in light of the individual circumstances presented by the patient, available diagnostic tools, accessible treatment options, and resources. Good Practice Statements: Diagnostic testing for OSA should be performed in conjunction with a comprehensive sleep evaluation and adequate follow-up. Polysomnography is the standard diagnostic test for the diagnosis of OSA in adult patients in whom there is a concern for OSA based on a comprehensive sleep evaluation.Recommendations: We recommend that clinical tools, questionnaires and prediction algorithms not be used to diagnose OSA in adults, in the absence of polysomnography or home sleep apnea testing. (STRONG). We recommend that polysomnography, or home sleep apnea testing with a technically adequate device, be used for the diagnosis of OSA in uncomplicated adult patients presenting with signs and symptoms that indicate an increased risk of moderate to severe OSA. (STRONG). We recommend that if a single home sleep apnea test is negative, inconclusive, or technically inadequate, polysomnography be performed for the diagnosis of OSA. (STRONG). We recommend that polysomnography, rather than home sleep apnea testing, be used for the diagnosis of OSA in patients with significant cardiorespiratory disease, potential respiratory muscle weakness due to neuromuscular condition, awake hypoventilation or suspicion of sleep related hypoventilation, chronic opioid medication use, history of stroke or severe insomnia. (STRONG). We suggest that, if clinically appropriate, a split-night diagnostic protocol, rather than a full-night diagnostic protocol for polysomnography be used for the diagnosis of OSA. (WEAK). We suggest that when the initial polysomnogram is negative and clinical suspicion for OSA remains, a second polysomnogram be considered for the diagnosis of OSA. (WEAK).

Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline

INTRODUCTION

This guideline establishes clinical practice recommendations for the diagnosis of obstructive sleep apnea (OSA) in adults and is intended for use in conjunction with other American Academy of Sleep Medicine (AASM) guidelines on the evaluation and treatment of sleep-disordered breathing in adults.

METHODS

The AASM commissioned a task force of experts in sleep medicine. A systematic review was conducted to identify studies, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) process was used to assess the evidence. The task force developed recommendations and assigned strengths based on the quality of evidence, the balance of benefits and harms, patient values and preferences, and resource use. In addition, the task force adopted foundational recommendations from prior guidelines as "good practice statements", that establish the basis for appropriate and effective diagnosis of OSA. The AASM Board of Directors approved the final recommendations.

RECOMMENDATIONS

The following recommendations are intended as a guide for clinicians diagnosing OSA in adults. Under GRADE, a STRONG recommendation is one that clinicians should follow under most circumstances. A WEAK recommendation reflects a lower degree of certainty regarding the outcome and appropriateness of the patient-care strategy for all patients. The ultimate judgment regarding propriety of any specific care must be made by the clinician in light of the individual circumstances presented by the patient, available diagnostic tools, accessible treatment options, and resources. Good Practice Statements: Diagnostic testing for OSA should be performed in conjunction with a comprehensive sleep evaluation and adequate follow-up. Polysomnography is the standard diagnostic test for the diagnosis of OSA in adult patients in whom there is a concern for OSA based on a comprehensive sleep evaluation.Recommendations: We recommend that clinical tools, questionnaires and prediction algorithms not be used to diagnose OSA in adults, in the absence of polysomnography or home sleep apnea testing. (STRONG). We recommend that polysomnography, or home sleep apnea testing with a technically adequate device, be used for the diagnosis of OSA in uncomplicated adult patients presenting with signs and symptoms that indicate an increased risk of moderate to severe OSA. (STRONG). We recommend that if a single home sleep apnea test is negative, inconclusive, or technically inadequate, polysomnography be performed for the diagnosis of OSA. (STRONG). We recommend that polysomnography, rather than home sleep apnea testing, be used for the diagnosis of OSA in patients with significant cardiorespiratory disease, potential respiratory muscle weakness due to neuromuscular condition, awake hypoventilation or suspicion of sleep related hypoventilation, chronic opioid medication use, history of stroke or severe insomnia. (STRONG). We suggest that, if clinically appropriate, a split-night diagnostic protocol, rather than a full-night diagnostic protocol for polysomnography be used for the diagnosis of OSA. (WEAK). We suggest that when the initial polysomnogram is negative and clinical suspicion for OSA remains, a second polysomnogram be considered for the diagnosis of OSA. (WEAK).

Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline

INTRODUCTION

This guideline establishes clinical practice recommendations for the diagnosis of obstructive sleep apnea (OSA) in adults and is intended for use in conjunction with other American Academy of Sleep Medicine (AASM) guidelines on the evaluation and treatment of sleep-disordered breathing in adults.

METHODS

The AASM commissioned a task force of experts in sleep medicine. A systematic review was conducted to identify studies, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) process was used to assess the evidence. The task force developed recommendations and assigned strengths based on the quality of evidence, the balance of benefits and harms, patient values and preferences, and resource use. In addition, the task force adopted foundational recommendations from prior guidelines as "good practice statements", that establish the basis for appropriate and effective diagnosis of OSA. The AASM Board of Directors approved the final recommendations.

RECOMMENDATIONS

The following recommendations are intended as a guide for clinicians diagnosing OSA in adults. Under GRADE, a STRONG recommendation is one that clinicians should follow under most circumstances. A WEAK recommendation reflects a lower degree of certainty regarding the outcome and appropriateness of the patient-care strategy for all patients. The ultimate judgment regarding propriety of any specific care must be made by the clinician in light of the individual circumstances presented by the patient, available diagnostic tools, accessible treatment options, and resources. Good Practice Statements: Diagnostic testing for OSA should be performed in conjunction with a comprehensive sleep evaluation and adequate follow-up. Polysomnography is the standard diagnostic test for the diagnosis of OSA in adult patients in whom there is a concern for OSA based on a comprehensive sleep evaluation.Recommendations: We recommend that clinical tools, questionnaires and prediction algorithms not be used to diagnose OSA in adults, in the absence of polysomnography or home sleep apnea testing. (STRONG). We recommend that polysomnography, or home sleep apnea testing with a technically adequate device, be used for the diagnosis of OSA in uncomplicated adult patients presenting with signs and symptoms that indicate an increased risk of moderate to severe OSA. (STRONG). We recommend that if a single home sleep apnea test is negative, inconclusive, or technically inadequate, polysomnography be performed for the diagnosis of OSA. (STRONG). We recommend that polysomnography, rather than home sleep apnea testing, be used for the diagnosis of OSA in patients with significant cardiorespiratory disease, potential respiratory muscle weakness due to neuromuscular condition, awake hypoventilation or suspicion of sleep related hypoventilation, chronic opioid medication use, history of stroke or severe insomnia. (STRONG). We suggest that, if clinically appropriate, a split-night diagnostic protocol, rather than a full-night diagnostic protocol for polysomnography be used for the diagnosis of OSA. (WEAK). We suggest that when the initial polysomnogram is negative and clinical suspicion for OSA remains, a second polysomnogram be considered for the diagnosis of OSA. (WEAK).

Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline

INTRODUCTION

This guideline establishes clinical practice recommendations for the diagnosis of obstructive sleep apnea (OSA) in adults and is intended for use in conjunction with other American Academy of Sleep Medicine (AASM) guidelines on the evaluation and treatment of sleep-disordered breathing in adults.

METHODS

The AASM commissioned a task force of experts in sleep medicine. A systematic review was conducted to identify studies, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) process was used to assess the evidence. The task force developed recommendations and assigned strengths based on the quality of evidence, the balance of benefits and harms, patient values and preferences, and resource use. In addition, the task force adopted foundational recommendations from prior guidelines as "good practice statements", that establish the basis for appropriate and effective diagnosis of OSA. The AASM Board of Directors approved the final recommendations.

RECOMMENDATIONS

The following recommendations are intended as a guide for clinicians diagnosing OSA in adults. Under GRADE, a STRONG recommendation is one that clinicians should follow under most circumstances. A WEAK recommendation reflects a lower degree of certainty regarding the outcome and appropriateness of the patient-care strategy for all patients. The ultimate judgment regarding propriety of any specific care must be made by the clinician in light of the individual circumstances presented by the patient, available diagnostic tools, accessible treatment options, and resources. Good Practice Statements: Diagnostic testing for OSA should be performed in conjunction with a comprehensive sleep evaluation and adequate follow-up. Polysomnography is the standard diagnostic test for the diagnosis of OSA in adult patients in whom there is a concern for OSA based on a comprehensive sleep evaluation.Recommendations: We recommend that clinical tools, questionnaires and prediction algorithms not be used to diagnose OSA in adults, in the absence of polysomnography or home sleep apnea testing. (STRONG). We recommend that polysomnography, or home sleep apnea testing with a technically adequate device, be used for the diagnosis of OSA in uncomplicated adult patients presenting with signs and symptoms that indicate an increased risk of moderate to severe OSA. (STRONG). We recommend that if a single home sleep apnea test is negative, inconclusive, or technically inadequate, polysomnography be performed for the diagnosis of OSA. (STRONG). We recommend that polysomnography, rather than home sleep apnea testing, be used for the diagnosis of OSA in patients with significant cardiorespiratory disease, potential respiratory muscle weakness due to neuromuscular condition, awake hypoventilation or suspicion of sleep related hypoventilation, chronic opioid medication use, history of stroke or severe insomnia. (STRONG). We suggest that, if clinically appropriate, a split-night diagnostic protocol, rather than a full-night diagnostic protocol for polysomnography be used for the diagnosis of OSA. (WEAK). We suggest that when the initial polysomnogram is negative and clinical suspicion for OSA remains, a second polysomnogram be considered for the diagnosis of OSA. (WEAK).

Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline

INTRODUCTION

This guideline establishes clinical practice recommendations for the diagnosis of obstructive sleep apnea (OSA) in adults and is intended for use in conjunction with other American Academy of Sleep Medicine (AASM) guidelines on the evaluation and treatment of sleep-disordered breathing in adults.

METHODS

The AASM commissioned a task force of experts in sleep medicine. A systematic review was conducted to identify studies, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) process was used to assess the evidence. The task force developed recommendations and assigned strengths based on the quality of evidence, the balance of benefits and harms, patient values and preferences, and resource use. In addition, the task force adopted foundational recommendations from prior guidelines as "good practice statements", that establish the basis for appropriate and effective diagnosis of OSA. The AASM Board of Directors approved the final recommendations.

RECOMMENDATIONS

The following recommendations are intended as a guide for clinicians diagnosing OSA in adults. Under GRADE, a STRONG recommendation is one that clinicians should follow under most circumstances. A WEAK recommendation reflects a lower degree of certainty regarding the outcome and appropriateness of the patient-care strategy for all patients. The ultimate judgment regarding propriety of any specific care must be made by the clinician in light of the individual circumstances presented by the patient, available diagnostic tools, accessible treatment options, and resources. Good Practice Statements: Diagnostic testing for OSA should be performed in conjunction with a comprehensive sleep evaluation and adequate follow-up. Polysomnography is the standard diagnostic test for the diagnosis of OSA in adult patients in whom there is a concern for OSA based on a comprehensive sleep evaluation.Recommendations: We recommend that clinical tools, questionnaires and prediction algorithms not be used to diagnose OSA in adults, in the absence of polysomnography or home sleep apnea testing. (STRONG). We recommend that polysomnography, or home sleep apnea testing with a technically adequate device, be used for the diagnosis of OSA in uncomplicated adult patients presenting with signs and symptoms that indicate an increased risk of moderate to severe OSA. (STRONG). We recommend that if a single home sleep apnea test is negative, inconclusive, or technically inadequate, polysomnography be performed for the diagnosis of OSA. (STRONG). We recommend that polysomnography, rather than home sleep apnea testing, be used for the diagnosis of OSA in patients with significant cardiorespiratory disease, potential respiratory muscle weakness due to neuromuscular condition, awake hypoventilation or suspicion of sleep related hypoventilation, chronic opioid medication use, history of stroke or severe insomnia. (STRONG). We suggest that, if clinically appropriate, a split-night diagnostic protocol, rather than a full-night diagnostic protocol for polysomnography be used for the diagnosis of OSA. (WEAK). We suggest that when the initial polysomnogram is negative and clinical suspicion for OSA remains, a second polysomnogram be considered for the diagnosis of OSA. (WEAK).

Receiver operating characteristics of impulse oscillometry parameters for predicting obstructive sleep apnea in preobese and obese snorers

Background

Inability to maintain upper-airway patency during sleep is a cause of obstructive sleep apnea (OSA) and its sequelae. The associated syndrome (OSAS) is common in obese populations, currently, nocturnal polysomnography is the gold standard for diagnosing this conditions, but the diagnostic procedures are expensive and time-consuming. Therefore, identification of new markers of OSAS would be useful. This study aims to examine the receiver operating characteristics of impulse oscillometry (IOS) parameters for the prediction of OSAS in preobese and obese snoring patients.

Methods

In total, 230 patients with normal spirometric values were included in this cross-sectional study. Full laboratory polysomnography was performed and IOS measurements were determined in sitting and supine positions to obtain respiratory impedance (Zrs), resistance (Rrs), and reactance (Xrs) parameters. The respiratory resistance at zero-frequency (Rrs0) was extrapolated by linear regression analysis of Rrs versus low-oscillatory-frequencies and its inverse, respiratory conductance (Grs), was calculated.

Results

In both the sitting and supine positions Rrs0, Zrs, and Rrs at five oscillatory-frequencies (Hz) and Grs, the reciprocal of Zrs5 (Gz), and Xrs at 5 Hz all had significant positive or negative correlations with OSAS severity as defined by the Respiratory disturbance index (RDI). The correlation coefficients between Rrs0, Zrs5, Rrs5, Grs, Gz, Xrs5 measured in the supine and RDI were 0.425, 0.395, 0.378, −0.425, −0.395, and −0.517, respectively (all p < 0.001). The receiver operating characteristics curves showed that Xrs at 5 Hz (reactance) in the supine position was the best for predicting OSAS with a sensitivity of 73 % and specificity of 84 % at the optimal cut-off point of −0.23 (kPa s L⁻¹). The other parameters also showed acceptable discriminating power. A logistic-regression model based on respiratory function abnormalities revealed that reactance combined with patient sex and lung volume yielded a specificity of 83.3 % with a sensitivity of 76.8 % for indicating OSAS.

Conclusion

Respiratory resistance and reactance measured by IOS are abnormal in preobese and obese OSAS patients, and these parameters are moderate to closely correlated with OSAS severity. IOS might be a useful screening tool for detecting OSAS in clinic based populations.

Impact of obstructive sleep apnea on lung volumes and mechanical properties of the respiratory system in overweight and obese individuals

BACKGROUND

Even through narrowing of the upper-airway plays an important role in the generation of obstructive sleep apnea (OSA), the peripheral airways is implicated in pre-obese and obese OSA patients, as a result of decreased lung volume and increased lung elastic recoil pressure, which, in turn, may aggravate upper-airway collapsibility.

METHODS

A total of 263 male (n = 193) and female (n = 70) subjects who were obese to various degrees without a history of lung diseases and an expiratory flow limitation, but troubled with snoring or suspicion of OSA were included in this cross-sectional study. According to nocturnal-polysomnography the subjects were distributed into OSA and non-OSA groups, and were further sub-grouped by gender because of differences between males and females, in term of, lung volume size, airway resistance, and the prevalence of OSA among genders. Lung volume and respiratory mechanical properties at different-frequencies were evaluated by plethysmograph and an impulse oscillation system, respectively.

RESULTS

Functional residual capacity (FRC) and expiratory reserve volume were significantly decreased in the OSA group compared to the non-OSA group among males and females. As weight and BMI in males in the OSA group were greater than in the non-OSA group (90 ± 14.8 kg vs. 82 ± 10.4 kg, p < 0.001; 30.5 ± 4.2 kg/m(2) vs. 28.0 ± 3.0 kg/m(2), p < 0.001), multiple regression analysis was required to adjust for BMI or weight and demonstrated that these lung volumes decreases were independent from BMI and associated with the severity of OSA. This result was further confirmed by the female cohort. Significant increases in total respiratory resistance and decreases in respiratory conductance (Grs) were observed with increasing severity of OSA, as defined by the apnea-hypopnea index (AHI) in both genders. The specific Grs (sGrs) stayed relatively constant between the two groups in woman, and there was only a weak association between AHI and sGrs among man. Multiple-stepwise-regression showed that reactance at 5 Hz was highly correlated with AHI in males and females or hypopnea index in females, independently-highly correlated with peripheral-airway resistance and significantly associated with decreasing FRC.

CONCLUSIONS

Total respiratory resistance and peripheral airway resistance significantly increase, and its inverse Grs decrease, in obese patients with OSA in comparison with those without OSA, and are independently associated with OSA severity. These results might be attributed to the abnormally increased lung elasticity recoil pressure on exhalation, due to increase in lung elasticity and decreased lung volume in obese OSA.

A clinical prediction formula for apnea-hypopnea index

Objectives. There are many studies regarding unnecessary polysomnography (PSG) when obstructive sleep apnea syndrome (OSAS) is suspected. In order to reduce unnecessary PSG, this study aims to predict the apnea-hypopnea index (AHI) via simple clinical data for patients who complain of OSAS symptoms. Method. Demographic, anthropometric, physical examination and laboratory data of a total of 390 patients (290 men, average age 50 ± 11) who were subject to diagnostic PSG were obtained and evaluated retrospectively. The relationship between these data and the PSG results was analyzed. A multivariate linear regression analysis was performed step by step to identify independent AHI predictors. Results. Useful parameters were found in this analysis in terms of body mass index (BMI), waist circumference (WC), neck circumference (NC), oxygen saturation measured by pulse oximetry (SpO₂), and tonsil size (TS) to predict the AHI. The formula derived from these parameters was the predicted AHI = (0.797 × BMI) + (2.286 × NC) − (1.272 × SpO₂) + (5.114 × TS) + (0.314 × WC). Conclusion. This study showed a strong correlation between AHI score and indicators of obesity. This formula, in terms of predicting the AHI for patients who complain about snoring, witnessed apneas, and excessive daytime sleepiness, may be used to predict OSAS prior to PSG and prevent unnecessary PSG.

Airway responsiveness measured by forced oscillation technique in severely obese patients, before and after bariatric surgery

BACKGROUND

The influence of obesity on airway responsiveness remains controversial.

OBJECTIVE

This study was designed to investigate airway responsiveness, airway inflammation, and the influence of sleep apnea syndrome (SAS), in severely obese subjects, before and after bariatric surgery.

METHODS

A total of 120 non-asthmatic obese patients were referred consecutively for pre-bariatric surgery evaluation. Lung function, airway responsiveness to methacholine, exhaled nitric oxide measurement, and sleep studies were performed. Airway hyperresponsiveness (AHR) was defined as a 50% or greater increase in respiratory resistance measured using the forced oscillation technique in response to a methacholine dose ≤ 2000 μg. Forced expiratory volume in 1 second (FEV₁) was measured after the last methacholine dose. Airway responsiveness was reevaluated after weight loss in patients with a pre-surgery AHR.

RESULTS

AHR was found in 16 patients. The percent FEV₁ decrease or percent respiratory resistance increase in response to methacholine was related to baseline expiratory airflow (forced expiratory flow at 50%) (r = 0.26, p < .006 and r = 0.315, p = .0005, respectively) but not to body mass index (BMI) or exhaled nitric oxide. Both airway responsiveness parameters were significantly related to forced expiratory flow at 25-75%/forced vital capacity, a measure of airway size relative to lung size (r = 0.27, p < .005 and r = 0.25, p < .007, respectively). Sleep apnea was not significantly associated with AHR or airway inflammation. About 11 patients with AHR were reevaluated 18 months to 2 years after surgery, with no change in AHR associated with weight loss.

CONCLUSION

Airway responsiveness is not related to BMI or to SAS. AHR in severely obese patients might be related to distal airway obstruction or low relative airway size.

A novel approach to prediction of mild obstructive sleep disordered breathing in a population-based sample: the Sleep Heart Health Study

This manuscript considers a data-mining approach for the prediction of mild obstructive sleep disordered breathing, defined as an elevated respiratory disturbance index (RDI), in 5,530 participants in a community-based study, the Sleep Heart Health Study. The prediction algorithm was built using modern ensemble learning algorithms, boosting in specific, which allowed for assessing potential high-dimensional interactions between predictor variables or classifiers. To evaluate the performance of the algorithm, the data were split into training and validation sets for varying thresholds for predicting the probability of a high RDI (≥7 events per hour in the given results). Based on a moderate classification threshold from the boosting algorithm, the estimated post-test odds of a high RDI were 2.20 times higher than the pre-test odds given a positive test, while the corresponding post-test odds were decreased by 52% given a negative test (sensitivity and specificity of 0.66 and 0.70, respectively). In rank order, the following variables had the largest impact on prediction performance: neck circumference, body mass index, age, snoring frequency, waist circumference, and snoring loudness.

Sleep apnea detection and classification using fuzzy logic: clinical evaluation

We have previously reported a system suitable for detection and classification of sleep apnea syndromes. This paper reports the results of the clinical evaluation of the proposed system. In the current implementation, the system uses breathing signals: nasal flow, thorax movement, and abdomen movement. The detection part of the system uses only the nasal flow signal to detect apnea employing two engines used in series. It then feeds segments labeled as abnormal to the classification part of the system, which uses the center of gravity of each segment to determine the type of abnormality: obstructive, central or hypopnea. In comparison to other systems, this implementation can be shown to be simpler and more accurate. When the low implementation cost is taken into consideration, the proposed system has a substantial potential for being used as a screening device.

Sleep apnea detection using an adaptive fuzzy logic based screening system

We report an adaptive diagnostic system for the classification of breathing events for the purpose of detecting sleep apnea syndromes. The system employs two classification engines used in series. The first engine is fuzzy logic-based and generates one of three outcomes for each breathing event: normal, abnormal, and not-sure. The second classification engine is based on a center of gravity engine which is trained using the normal and abnormal events, generated by the first engine, and is specifically designed for sorting out the not-sure events. The fuzzy logic engine can be tuned very conservatively to reduce or eliminate the chance of error at the first stage. Since the second engine is trained adaptively using normal and abnormal data of the same patient, its accuracy is generally better than relying on multi-patient training approaches. The two-step, adaptive nature of the system allows for high accuracy and lends itself well for practical implementation.

FEF25–75/FVC measurements and extrathoracic airway obstruction in obstructive sleep apnea patients

The aims of this study were to evaluate patients with obstructive sleep apnea syndrome (OSAS) with regards to dysanapsis (airway size relative to lung size) and to demonstrate the differences between the patients with and without extrathoracic airway obstruction. The study population consisted of 15 patients with OSAS and 14 age and body mass index (BMI) matched control subjects. OSAS patients and control subjects showed similar characteristics in FEV(1), FEV(1)/FVC, FEF(25-75), and FEF(25-75)/FVC ratios. Expiration reserve volume was significantly higher in the control group than in OSAS patients (p<0.01). Six patients exhibited extrathoracic airway obstruction while awake. Of these, three had also a sawtooth pattern in their flow-volume curves. The remaining nine patients had no extrathoracic airway obstruction and had lower apnea-hypopnea indexes (AHI) than the obstruction group (p<0.05). OSAS patients and age- and BMI-matched healthy controls had similar characteristics in terms of dysanapsis. In addition, there was no relation between the FEF(25-75)/FVC ratio and AHI, MinO(2), and MeanO(2). Extrathoracic airway obstruction may be a feature of only severe OSAS patients.

Operating characteristics of the negative expiratory pressure technique in predicting obstructive sleep apnoea syndrome in snoring patients

BACKGROUND

This study examines the operating characteristics of the expiratory flow response to a negative pressure (NEP) applied to the mouth in the prediction of obstructive sleep apnoea syndrome (OSAS) in snoring patients.

METHODS

Two hundred and thirty eight patients with normal spirometric values were studied. Full laboratory polysomnography was performed and an NEP of -5 cm H(2)O was applied in the sitting and supine positions.

RESULTS

A significant correlation was found between the degree of flow limitation measured by NEP in both positions (expressed as the percentage of the expired tidal volume over which NEP induced flow did not exceed spontaneous flow) and the apnoea-hypopnoea index (AHI). This correlation was significantly higher in the supine position (p<0.0001) where an expiratory flow limitation cut off value of >/=27.5% of the tidal volume produced a sensitivity of 81.9% and a specificity of 69.1% in predicting OSAS.

CONCLUSION

These findings show that the degree of instability of the upper airway measured by NEP is correlated with the severity of OSAS. NEP had moderate sensitivity and specificity and may be useful in predicting OSAS in a clinic based population.

Pulmonary function and sleep apnea

The purpose of this study was to determine whether there is a relationship between pulmonary function measured during wakefulness and sleep apnea. We prospectively studied 1296 patients, who were free of any lung disease, referred to our sleep clinic for evaluation of possible sleep apnea. All patients had in-hospital nocturnal polysomnography and pulmonary function measurements, which included flow-volume curve, body plethysmography, and single-breath diffusing capacity for carbon monoxide. The results were analyzed by comparing pulmonary function data between four groups of patients, grouped according to apnea severity as reflected by their apnea/hypopnea index: nonapneics (apnea-hypopnea index </= 10), mild apnea (10 < apnea-hypopnea index </= 30), moderate apnea (30 < apnea-hypopnea index </= 50), and severe apnea (apnea-hypopnea index > 50). Analysis of covariance demonstrated no difference in any of the pulmonary function parameters between the four groups, after adjusting for age, body mass index, or weight as the covariate. We conclude that in nonsmoking patients without lung disease, sleep apnea is unrelated to pulmonary function measured during wakefulness.

Valor predictivo de la clínica para la identificación de los pacientes con síndrome de apneas-hipopneas durante el sueño susceptibles de tratamiento con presión positiva continua de la vía aérea (CPAP)

OBJECTIVE

To analyze the predictive value of clinical data for identifying patients suspected of sleep apnea-hypopnea syndrome with an apnea-hypopnea index (AHI)> or = 30.

MATERIAL AND METHODS

Patient characteristics, cardiorespiratory medical history, and clinical signs and symptoms were recorded for all patients. Exclusion criteria were daytime respiratory insufficiency or heart failure. All patients underwent polysomnographic testing (AutoSet Portable Plus II, ResMed Corp, Sydney, Australia) for automatic AHI calculation and manual determination of central and obstructive apneas. A logistic regression model was constructed to calculate the likelihood of an individual's presenting an AHI> or = 30 as well as the predictive value of each variable and of the final model.

RESULTS

Three hundred twenty-nine patients with a mean +/- SD age of 58 +/- 13.45 years were studied; 76.4% were men. Data for 207 patients were used to construct the logistic regression model: logit (P) = 2.5 blood pressure + 1.5 Epworth test + body mass index + 0.6 repeated observed episodes of apnea 2.1. Logit(P) was loge (1-P)/P and variables were dichotomized with cut points of 11 for the Epworth test and of 30 kg/m2 for body mass index. The diagnostic sensitivity of the model was 80.2% (75%-86%), specificity was 93.4% (89%-95%), positive predictive value was 89.6% (84%-93%) and negative predictive value was 86.9% (81%-90%), such that 89.6% of the patients were correctly classified. The variable with the greatest predictive value was high blood pressure. The model was validated prospectively in the remaining 102 patients.

CONCLUSIONS

Prior to diagnostic tests for SAHS, clinical data can be useful for identifying patients suspected to have a AHI> or = 30.

Prediction formulae for sleep-disordered breathing

Prediction formulae for sleep-disordered breathing can be useful for excluding a diagnosis, establishing an a priori probability of having a positive test, and for prioritizing patient testing. In general, prediction models have high sensitivity but low specificity. In a study analyzing the performance of four previously described prediction models, sensitivities ranged from 76% to 96%, specificities ranged from 13% to 54%, while positive predictive values ranged between 69% and 77%. The models were useful in identifying patients with a respiratory disturbance index of > or = 20 so that these patients could undergo alternative diagnostic testing strategies. The Berlin Questionnaire was tested in primary care settings and was able to identify high-risk patients fairly accurately. A regression neural network performed well with a sensitivity of 99%, a specificity of 80%, a positive predictive value of 88%, and a negative predictive value of 98%. In obese snorers, a regression model utilizing daytime arterial O2 saturation and specific respiratory conductance was effective for excluding obstructive sleep apnea (OSA). In congestive heart failure patients, risk factors for central sleep apnea include male gender, atrial fibrillation, age >60 years, and wake time PaCO2 <38 mm Hg. In children, risk factors for sleep apnea include obesity, African-American race, sinus problems, and persistent wheezing. There are also racial anthropomorphic differences in OSA patients, with whites having a tendency towards brachycephaly facial types (reduced anterior-posterior cranial dimension) and African-Americans having a tendency toward leptoproscopic facial types (longer facial height and decreased facial width). Further refinement of prediction formulae will improve diagnostic accuracy.